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\begin{slide}
  \slidetitle{Introduction}\\
  \begin{itemize}
  \item SVM-KM is characterized by the employment of $k$-means as 
				a pre-selection method before training of SVMs.
  \item SVM-KM considers the SVM topology when choosing the subset.
  \end{itemize}
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	\begin{itemize}
  \item Clusters with mixed composition are likely to 
  			happen near the separation margins.
  \item Cluster with mixed composition are likely to
  			contain some support vectors.
  \end{itemize}

	\hot{Ideas:}
	\begin{itemize}
  \item Clusters with mixed composition are likely to 
  			happen near the separation margins.
  \item Cluster with mixed composition are likely to
  			contain some support vectors.
  \end{itemize}	  
  \ho 
	\begin{itemize}
		\item $\lambda$: source of information
		\item Bayes Rule: the way how the information is added.
	\end{itemize}
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